AEVisionLab: Manipulating In-vehicle Ethernet Networks with All-round Vision

With the increasing adoption of Advanced Driver Assistance Systems (ADAS) in modern cars, the use of vision systems for autonomous vehicles, driving assistance, and in-vehicle entertainment has introduced new risks and attack vectors to existing In-Vehicle Networks (IVNs), thus bringing considerable concerns to the automotive cybersecurity space. Prior works have focused on analyzing functional or partial security aspects of vision systems during ADAS simulation using specialized Automotive Ethernet (AE) equipment or requiring expensive vehicle-in-the-loop setups. These approaches are either inaccessible to independent security researchers or do not offer comprehensive insights to help researchers understand the practical implications of attacks in a realistic car employing Automotive Ethernet IVNs for vision-related use cases. To this end, we build an embedded system, AEVisionLab , using automotive commercial off-the-shelf (COTS) electronic control units (ECUs) and a workstation. AEVisionLab is a standalone, reusable, and comprehensive test platform for Automotive Ethernet (AE) networks that allow manipulation of messages over a realistic All-round Vision IVN of a BMW G20 via 100BASE-T1 Ethernet bridging. AEVisionLab allows replication of driving test scenarios directly with COTS ECUs and collection of key network performance metrics, facilitating the design, evaluation, and impact analysis of concrete attacks in the laboratory. We demonstrate the capability of AEVisionLab by designing and evaluating concrete attacks scenarios including eavesdropping and hijacking of SOME/IP services, manipulation and delaying video feed, among others. We envision AEVisionLab as a flexible platform for designing and evaluating both attack and mitigation techniques (e.g., intrusion detection) on AE network, which can be easily extended to support other automotive ECUs, machine learning models for ADAS, or sensors for assisted driving.